The Impact of Contextual Information on the Accuracy of Existing Recommender Systems for Web Personalization

Traditionally, recommender systems for the Web deal with applications that have two types of entities/dimensions, users and items. With these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a direct method that enriches the information in the access logs with new dimensions. We empirically test this method with two recommender systems, an item-based collaborative filtering technique and association rules, on three data sets. Our results show that while collaborative filtering is not able to take advantage of the new dimensions added, association rules are capable of profiting from our direct method.